Is Cambodia the World's Largest Cashew Producer?
- URL: http://arxiv.org/abs/2405.16926v1
- Date: Mon, 27 May 2024 08:20:50 GMT
- Title: Is Cambodia the World's Largest Cashew Producer?
- Authors: Veasna Chaya, Ate Poortinga, Keo Nimol, Se Sokleap, Mon Sophorn, Phy Chhin, Andrea McMahon, Andrea Puzzi Nicolau, Karis Tenneson, David Saah,
- Abstract summary: This study addresses the gap in detailed land use data for cashew plantations in Cambodia.
We collected over 80,000 training polygons across Cambodia to train a convolutional neural network for precise cashew plantation mapping.
Cambodia ranks among the top five in terms of cultivated area and the top three in global cashew production, driven by high yields.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cambodia's agricultural landscape is rapidly transforming, particularly in the cashew sector. Despite the country's rapid emergence and ambition to become the largest cashew producer, comprehensive data on plantation areas and the environmental impacts of this expansion are lacking. This study addresses the gap in detailed land use data for cashew plantations in Cambodia and assesses the implications of agricultural advancements. We collected over 80,000 training polygons across Cambodia to train a convolutional neural network using high-resolution optical and SAR satellite data for precise cashew plantation mapping. Our findings indicate that Cambodia ranks among the top five in terms of cultivated area and the top three in global cashew production, driven by high yields. Significant cultivated areas are located in Kampong Thom, Kratie, and Ratanak Kiri provinces. Balancing rapid agricultural expansion with environmental stewardship, particularly forest conservation, is crucial. Cambodia's cashew production is poised for further growth, driven by high-yielding trees and premium nuts. However, sustainable expansion requires integrating agricultural practices with economic and environmental strategies to enhance local value and protect forested areas. Advanced mapping technologies offer comprehensive tools to support these objectives and ensure the sustainable development of Cambodia's cashew industry.
Related papers
- Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system.
We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
arXiv Detail & Related papers (2024-11-07T22:29:05Z) - The unrealized potential of agroforestry for an emissions-intensive agricultural commodity [48.652015514785546]
We use machine learning to generate estimates of shade-tree cover and carbon stocks across a West African region.
We find that existing shade-tree cover is low, and not spatially aligned with climate threat.
But we also find enormous unrealized potential for the sector to counterbalance a large proportion of their high carbon footprint annually.
arXiv Detail & Related papers (2024-10-28T10:02:32Z) - Multispectral Fine-Grained Classification of Blackgrass in Wheat and Barley Crops [2.580056799681784]
Blackgrass is a major grass weed which causes particular problems in cereal crops in north-west Europe.
With the use of machine vision and multispectral imaging, we investigate the effectiveness of state-of-the-art methods to identify blackgrass.
arXiv Detail & Related papers (2024-05-03T16:23:41Z) - Climate Change Impact on Agricultural Land Suitability: An Interpretable
Machine Learning-Based Eurasia Case Study [94.07737890568644]
As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition.
Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages.
Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
arXiv Detail & Related papers (2023-10-24T15:15:28Z) - HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using
Harvest Piles and Remote Sensing [50.4506590177605]
HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023.
We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems.
We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
arXiv Detail & Related papers (2023-08-23T11:03:28Z) - Agave crop segmentation and maturity classification with deep learning
data-centric strategies using very high-resolution satellite imagery [101.18253437732933]
We present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery.
We solve real-world deep learning problems in the very specific context of agave crop segmentation.
With the resulting accurate models, agave production forecasting can be made available for large regions.
arXiv Detail & Related papers (2023-03-21T03:15:29Z) - Mapping smallholder cashew plantations to inform sustainable tree crop
expansion in Benin [4.008214303620168]
Cashews are grown by over 3 million smallholders in more than 40 countries worldwide.
Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings.
By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, we produced the first national map of cashew in Benin.
arXiv Detail & Related papers (2023-01-01T07:18:47Z) - Neuroevolution-based Classifiers for Deforestation Detection in Tropical
Forests [62.997667081978825]
Millions of hectares of tropical forests are lost every year due to deforestation or degradation.
Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals.
This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
arXiv Detail & Related papers (2022-08-23T16:04:12Z) - Satellite-based high-resolution maps of cocoa planted area for C\^ote
d'Ivoire and Ghana [18.426247380427846]
cocoa cultivation is an underlying driver of over 37% and 13% of forest loss in protected areas in Cote d'Ivoire and Ghana.
These maps serve as a crucial building block to advance understanding of conservation and economic development in cocoa producing regions.
arXiv Detail & Related papers (2022-06-13T12:58:35Z) - High carbon stock mapping at large scale with optical satellite imagery
and spaceborne LIDAR [27.25600860698314]
Deforestation, which causes high carbon emissions and threatens biodiversity, is often linked to agricultural expansion.
We propose an automated approach that aims to support conservation and sustainable land use planning decisions.
A deep learning approach is developed that estimates canopy height for each 10 m Sentinel-2 pixel by learning from sparse GEDI LIDAR reference data.
We show that these wall-to-wall maps of canopy top height are predictive for classifying HCS forests and degraded areas with an overall accuracy of 86 % and produce a first high carbon stock map for Indonesia, Malaysia, and the Philippines.
arXiv Detail & Related papers (2021-07-15T16:21:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.